AICov: An Integrative Deep Learning Framework for COVID-19 Forecasting with Population Covariates

TL;DR

AICov is a deep learning framework that enhances COVID-19 forecasting by integrating diverse population covariates, including socioeconomic and health factors, leading to improved prediction accuracy.

Contribution

This work introduces AICov, a novel integrative deep learning model that incorporates multiple population-level covariates for more accurate COVID-19 forecasting.

Findings

Improved prediction accuracy with integrated covariates

Effective use of LSTM-based deep learning strategies

Demonstrated benefits of socioeconomic and health data integration

Abstract

The COVID-19 pandemic has profound global consequences on health, economic, social, political, and almost every major aspect of human life. Therefore, it is of great importance to model COVID-19 and other pandemics in terms of the broader social contexts in which they take place. We present the architecture of AICov, which provides an integrative deep learning framework for COVID-19 forecasting with population covariates, some of which may serve as putative risk factors. We have integrated multiple different strategies into AICov, including the ability to use deep learning strategies based on LSTM and even modeling. To demonstrate our approach, we have conducted a pilot that integrates population covariates from multiple sources. Thus, AICov not only includes data on COVID-19 cases and deaths but, more importantly, the population's socioeconomic, health and behavioral risk factors at a local level. The compiled data are fed into AICov, and thus we obtain improved prediction by integration of the data to our model as compared to one that only uses case and death data.